Welcome to my blog where I’m going to take you on a journey through my experience on the 5th and the last day of dashboard week.

So, the challenge for the day was to work on the data of shark attacks for past two centuries. I was under the assumption that last day data is going to be nice and clean. However, as soon as I got the data, all my assumptions were gone.  I realized that it was inconsistent and needed a lot of cleaning and grouping. But, I wasn’t going to let that bring me down. I knew I had to come up with a plan of action to tackle this data challenge.

Data Preparation

After initial cleaning of the data in Alteryx I decided to explore different activities that people were doing before getting attacked by sharks. I created a calculated field to group the activities. Similarly created a calculated field to define shark species and started working on visualizing the data.


I began by creating a map that showed the number of attacks by country. This helped me understand which countries had the highest number of shark attacks. Next, I created a line chart that showed the number of attacks over the years, with a set action for the activities. This allowed me to see the trends in the data and understand which activities were the most risky.

Moving on, I created a month-wise attack visualization to see if there were any patterns in the data. This helped me identify which months had the highest number of attacks.

To make the visualization more interactive, I added an activity filter that worked on a donut chart showing the fatality rate, a bar chart showing the victims age group and the different types of sharks. This allowed the users to filter the data based on the activity and get a better understanding of the fatality rate and the types of sharks that were involved.


In conclusion, dashboard week was an exhausting but incredibly valuable experience. I learned how to work with new datasets, create engaging visualizations, and tell a story using data. I’m excited to take the skills and knowledge I gained during this week and apply them to future data analysis projects.

Seema Keswani
Author: Seema Keswani